A Holistic Indicator of Polarization to Measure Online Sexism
- URL: http://arxiv.org/abs/2404.02205v2
- Date: Sat, 29 Jun 2024 15:27:34 GMT
- Title: A Holistic Indicator of Polarization to Measure Online Sexism
- Authors: Vahid Ghafouri, Jose Such, Guillermo Suarez-Tangil,
- Abstract summary: The online trend of the manosphere and feminist discourse on social networks requires a holistic measure of the level of sexism in an online community.
This indicator is important for policymakers and moderators of online communities.
We build a model that can provide a comparable holistic indicator of toxicity targeted toward male and female identity and male and female individuals.
- Score: 2.498836880652668
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The online trend of the manosphere and feminist discourse on social networks requires a holistic measure of the level of sexism in an online community. This indicator is important for policymakers and moderators of online communities (e.g., subreddits) and computational social scientists, either to revise moderation strategies based on the degree of sexism or to match and compare the temporal sexism across different platforms and communities with real-time events and infer social scientific insights. In this paper, we build a model that can provide a comparable holistic indicator of toxicity targeted toward male and female identity and male and female individuals. Despite previous supervised NLP methods that require annotation of toxic comments at the target level (e.g. annotating comments that are specifically toxic toward women) to detect targeted toxic comments, our indicator uses supervised NLP to detect the presence of toxicity and unsupervised word embedding association test to detect the target automatically. We apply our model to gender discourse communities (e.g., r/TheRedPill, r/MGTOW, r/FemaleDatingStrategy) to detect the level of toxicity toward genders (i.e., sexism). Our results show that our framework accurately and consistently (93% correlation) measures the level of sexism in a community. We finally discuss how our framework can be generalized in the future to measure qualities other than toxicity (e.g. sentiment, humor) toward general-purpose targets and turn into an indicator of different sorts of polarizations.
Related papers
- Anti-Sexism Alert System: Identification of Sexist Comments on Social
Media Using AI Techniques [0.0]
Sexist comments that are publicly posted in social media (newspaper comments, social networks, etc.) usually obtain a lot of attention and become viral, with consequent damage to the persons involved.
In this paper, we introduce an anti-sexism alert system, based on natural language processing (NLP) and artificial intelligence (AI)
This system analyzes any public post, and decides if it could be considered a sexist comment or not.
arXiv Detail & Related papers (2023-11-28T19:48:46Z) - Probing Explicit and Implicit Gender Bias through LLM Conditional Text
Generation [64.79319733514266]
Large Language Models (LLMs) can generate biased and toxic responses.
We propose a conditional text generation mechanism without the need for predefined gender phrases and stereotypes.
arXiv Detail & Related papers (2023-11-01T05:31:46Z) - "I'm fully who I am": Towards Centering Transgender and Non-Binary
Voices to Measure Biases in Open Language Generation [69.25368160338043]
Transgender and non-binary (TGNB) individuals disproportionately experience discrimination and exclusion from daily life.
We assess how the social reality surrounding experienced marginalization of TGNB persons contributes to and persists within Open Language Generation.
We introduce TANGO, a dataset of template-based real-world text curated from a TGNB-oriented community.
arXiv Detail & Related papers (2023-05-17T04:21:45Z) - Social Biases in Automatic Evaluation Metrics for NLG [53.76118154594404]
We propose an evaluation method based on Word Embeddings Association Test (WEAT) and Sentence Embeddings Association Test (SEAT) to quantify social biases in evaluation metrics.
We construct gender-swapped meta-evaluation datasets to explore the potential impact of gender bias in image caption and text summarization tasks.
arXiv Detail & Related papers (2022-10-17T08:55:26Z) - Annotators with Attitudes: How Annotator Beliefs And Identities Bias
Toxic Language Detection [75.54119209776894]
We investigate the effect of annotator identities (who) and beliefs (why) on toxic language annotations.
We consider posts with three characteristics: anti-Black language, African American English dialect, and vulgarity.
Our results show strong associations between annotator identity and beliefs and their ratings of toxicity.
arXiv Detail & Related papers (2021-11-15T18:58:20Z) - #ContextMatters: Advantages and Limitations of Using Machine Learning to
Support Women in Politics [0.15749416770494704]
ParityBOT was deployed across elections in Canada, the United States and New Zealand.
It was used to analyse and classify more than 12 million tweets directed at women candidates and counter toxic tweets with supportive ones.
We examine the rate of false negatives, where ParityBOT failed to pick up on insults directed at specific high profile women.
arXiv Detail & Related papers (2021-09-30T22:55:49Z) - SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection [9.443571652110663]
We propose the first Chinese sexism dataset -- Sina Weibo Sexism Review (SWSR) dataset -- and a large Chinese lexicon SexHateLex.
SWSR dataset provides labels at different levels of granularity including (i) sexism or non-sexism, (ii) sexism category and (iii) target type.
We conduct experiments for the three sexism classification tasks making use of state-of-the-art machine learning models.
arXiv Detail & Related papers (2021-08-06T12:06:40Z) - Mitigating Biases in Toxic Language Detection through Invariant
Rationalization [70.36701068616367]
biases toward some attributes, including gender, race, and dialect, exist in most training datasets for toxicity detection.
We propose to use invariant rationalization (InvRat), a game-theoretic framework consisting of a rationale generator and a predictor, to rule out the spurious correlation of certain syntactic patterns.
Our method yields lower false positive rate in both lexical and dialectal attributes than previous debiasing methods.
arXiv Detail & Related papers (2021-06-14T08:49:52Z) - Gender Stereotype Reinforcement: Measuring the Gender Bias Conveyed by
Ranking Algorithms [68.85295025020942]
We propose the Gender Stereotype Reinforcement (GSR) measure, which quantifies the tendency of a Search Engines to support gender stereotypes.
GSR is the first specifically tailored measure for Information Retrieval, capable of quantifying representational harms.
arXiv Detail & Related papers (2020-09-02T20:45:04Z) - "Call me sexist, but...": Revisiting Sexism Detection Using
Psychological Scales and Adversarial Samples [2.029924828197095]
We outline the different dimensions of sexism by grounding them in their implementation in psychological scales.
From the scales, we derive a codebook for sexism in social media, which we use to annotate existing and novel datasets.
Results indicate that current machine learning models pick up on a very narrow set of linguistic markers of sexism and do not generalize well to out-of-domain examples.
arXiv Detail & Related papers (2020-04-27T13:07:46Z) - Text-mining forma mentis networks reconstruct public perception of the
STEM gender gap in social media [0.0]
Textual forma mentis networks (TFMNs) are glass boxes introduced for extracting, representing and understanding mindsets' structure.
TFMNs were applied to the case study of the gender gap in science, which was strongly linked to distorted mindsets by recent studies.
arXiv Detail & Related papers (2020-03-18T13:39:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.